Robust segmentation of the ribcage may be important for automated analysis of chest radiographs. There are many difficulties that any method must contend with if consistent and reliable results are to be achieved. Most prior work has mainly relied on edge detection or direct segmentation of the air-filled lungs. These methods have little or no chance of working robustly on general radiographs, especially those acquired using portable x-ray devices.
One algorithm that attempts to address the general problem of image segmentation was detailed in the 2000 publication by van Ginneken, “Automatic Segmentation of Lung Fields in Chest Radiographs.” In this paper, a series of rules are combined with pixel classification in order to segment the lung fields. The top section of the rib cage may be detected by using dynamic programming in the polar domain and second derivative information may be used in doing so. However, the techniques used in this paper tend to be sensitive to artifacts, due to the sole use of second derivative information. They also tend to be sensitive with respect to, for example, variations in contrast. Therefore, there remains room for improvement with respect to such algorithms.